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Original Article

The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network

인공신경망을 이용한 지하수위 예측과 계절효과 반영을 위한 입력치의 영향

Incheol Kim1
,
Junhwan Lee2*
김 인철1
,
이 준환2*
1School of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Korea
2School of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Korea
1연세대학교 사회환경시스템공학과 박사과정
2연세대학교 사회환경시스템공학과 교수
* Corresponding Author
30 September 2018. pp. 125-133
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Abstract

Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

Keywords
Artificial neural network
Hydrological factor
Performance
Seasonal effect

인공신경망 (Artificial neural network, ANN)은 간편히 시계열 데이터를 예측할 수 있는 모델 중에 하나로 지하수위를 예측하는데 빈번히 사용되었으며, 많은 연구자들이 ANN으로 지하수위 예측에 있어서 높은 예측 신뢰성을 얻기 위하여 노력해 왔다. 본 연구에서는 ANN를 이용한 지하수위 예측 시 계절 효과를 반영하기 위한 input으로 사용되는 Dummy가 지하수위 예측 결과에 미치는 영향에 대하여 분석하였다. 정성적 및 정량적인 분석을 위하여 도해법과 상관계수, 에러 지수를 이용하였다. 분석결과 하천변 도심지역에서는 ANN의 input으로 사용된 Dummy가 오히려 예측 신뢰성을 떨어뜨리는 결과를 보였다.

키워드
인공신경망
수문인자
예측 신뢰성
계절효과
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Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 5
  • No :3
  • Pages :125-133
  • Received Date : 2018-09-11
  • Revised Date : 2018-09-18
  • Accepted Date : 2018-09-18
  • DOI :https://doi.org/10.17820/eri.2018.5.3.125
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